ABOUT FLOX
Flox builds adaptive intelligence that interprets and responds to wildlife in real time. Our edge hardware and AI platform create a closed learning loop: field data → models → deterrent actions → continuous adaptation. We protect high-value infrastructure while reducing ecological loss.
Role Overview
This role drives the integration of AI into real-world systems. You engineer and optimize inference pipelines on Flox Edge, unify multi-modal signals, and close the loop between deployed behaviour, cloud-based learning, and field performance. You turn research outputs into operational intelligence.
What You’ll Do
- Implement and optimize real-time inference on embedded edge hardware under power, latency, and memory constraints
- Build the signal-generation layer: convert model outputs into structured, species-specific deterrent actions
- Design and maintain the cloud-to-edge model lifecycle: data ingestion, versioning, updates, validation, and rollout
- Develop monitoring, telemetry, and drift-detection systems to track field performance and trigger adaptation
- Collaborate with AI infrastructure and model-training engineers to define model requirements, integration points, and deployment constraints
- Build multi-modal data-fusion pipelines (audio, video, motion) to improve behavioural prediction and species interpretation
- Establish performance benchmarks for deployed models and iterate to meet real-world reliability targets
What You Bring
- Strong experience deploying and optimizing ML systems on embedded or edge devices
- Proficiency in inference optimization (quantization, pruning, profiling, memory management)
- Experience integrating models into production systems with strict hardware and reliability demands
- Competence working with multi-modal data streams and real-time processing
- Solid engineering skills in Python and C++ (or similar), plus familiarity with embedded OS/RTOS environments
- Understanding of cloud-to-edge workflows and remote update mechanisms
- Ability to take full ownership from architecture to field-ready execution
- Fluent in English
Why Join Flox
You work on applied intelligence with real-world stakes. Every device deployed strengthens the system. The data you help capture and the models you help run generate compounding advantage and measurable ecological and economic impact.
Environment
Hands-on engineering culture. High autonomy. Direct responsibility for systems that run in the field, not in simulation. Fast iteration with clear feedback from deployments.
Location & Flexibility
Based in Stockholm with hybrid flexibility. Field visits and hardware interaction when required.
Role Impact
You make the Wildlife Brain operational. You turn intelligence into action. You push the boundary of what adaptive models can achieve on constrained hardware in uncontrolled environments.
When you apply, in your cover letter, include a short paragraph on each of these:
- The last system you built end-to-end that worked in the real world
- A failure you were responsible for and the correction you made
- The specific aspect of human–wildlife conflict that drives you to work on this problem
